import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# versi pandas, matplotlib, seaborn device agung
print("matplotlib version:", matplotlib.__version__)
print("pandas version:", pd.__version__)
print("seaborn version:", sns.__version__)

matplotlib version: 3.9.4
pandas version: 2.3.0+4.g1dfc98e16a
seaborn version: 0.13.2

# Import dataset ke dataframe agung
agung = pd.read_csv('Homework_Dataset.csv')
# tampilkan agung isinya
agung.head()

#analisa dikit
agung.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

agung['MonthlyCharges'].describe()

count    7043.000000
mean       64.761692
std        30.090047
min        18.250000
25%        35.500000
50%        70.350000
75%        89.850000
max       118.750000
Name: MonthlyCharges, dtype: float64

agung['PaymentMethod'].unique()

array(['Electronic check', 'Mailed check', 'Bank transfer (automatic)',
       'Credit card (automatic)'], dtype=object)

gemeter = agung.groupby('PaymentMethod')['MonthlyCharges'].mean().reset_index()
gemeter

gemeter2 = agung.groupby('PaymentMethod')['MonthlyCharges'].median().reset_index()
gemeter2

# urutan kategori berdasarkan rata-rata untuk plot
mean_order = gemeter.groupby('PaymentMethod')['MonthlyCharges'].mean().sort_values(ascending=False).index

# urutan kategori berdasarkan median untuk plot
median_order = gemeter2.groupby('PaymentMethod')['MonthlyCharges'].median().sort_values(ascending=False).index

sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter, order=mean_order)
plt.title('Rata-rata MonthlyCharges per PaymentMethod')
plt.xticks(rotation=45)

([0, 1, 2, 3],
 [Text(0, 0, 'Electronic check'),
  Text(1, 0, 'Bank transfer (automatic)'),
  Text(2, 0, 'Credit card (automatic)'),
  Text(3, 0, 'Mailed check')])

sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter2, order=median_order)
plt.title('Median MonthlyCharges per PaymentMethod')
plt.xticks(rotation=45)

([0, 1, 2, 3],
 [Text(0, 0, 'Electronic check'),
  Text(1, 0, 'Bank transfer (automatic)'),
  Text(2, 0, 'Credit card (automatic)'),
  Text(3, 0, 'Mailed check')])

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))

sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter, ax=axes[0], hue='PaymentMethod', order=mean_order, palette='deep')

sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter2, ax=axes[1], hue='PaymentMethod', order=median_order,  palette='deep')

axes[0].set_title('Rata-rata MonthlyCharges per PaymentMethod', fontsize=10, fontweight='bold')
axes[0].set_ylim(0, 85)
axes[0].tick_params(axis='x', rotation=40, labelsize=8)


axes[1].set_title('Median MonthlyCharges per PaymentMethod', fontsize=10, fontweight='bold')
axes[1].set_ylim(0, 85)
axes[1].set_ylabel("")
axes[1].tick_params(axis='x', rotation=40, labelsize=8)

plt.tight_layout()
plt.show()

#gpt is the way
import matplotlib.patches as mpatches

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))

colors = sns.color_palette('deep')
payment_methods = ['Bank transfer (automatic)', 'Credit card (automatic)', 'Electronic check', 'Mailed check']
palette_dict = dict(zip(payment_methods, colors))

# rata-rata
sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter, ax=axes[0], hue='PaymentMethod', order=mean_order, palette=palette_dict)

# median
sns.barplot(x='PaymentMethod', y='MonthlyCharges', data=gemeter2, ax=axes[1], hue='PaymentMethod', order=median_order, palette=palette_dict)

axes[0].set_title('Rata-rata MonthlyCharges per PaymentMethod', fontsize=10, fontweight='bold')
axes[0].set_ylim(0, 85)
axes[0].set_xticks([])

axes[1].set_title('Median MonthlyCharges per PaymentMethod', fontsize=10, fontweight='bold')
axes[1].set_ylim(0, 85)
axes[1].set_ylabel("")
axes[1].set_xticks([])

legend_patches = [mpatches.Patch(color=palette_dict[pm], label=pm) for pm in payment_methods]
axes[1].legend(handles=legend_patches, loc='lower right', title='Payment Method', fontsize=8, title_fontsize=10)

plt.tight_layout()
plt.show()

agung.groupby('PaymentMethod')['MonthlyCharges'].describe()

#q1 dan q3
agung.groupby('PaymentMethod')['MonthlyCharges'].agg(lambda x: (x.quantile(0.25), x.quantile(0.75)))

PaymentMethod
Bank transfer (automatic)            (41.0875, 92.9625)
Credit card (automatic)                (38.6625, 90.85)
Electronic check                          (60.15, 94.7)
Mailed check                 (20.15, 64.91250000000001)
Name: MonthlyCharges, dtype: object

#mengecek upper bound dan lower bound untuk masing masing PaymentMethod
def calculate_bounds(group):
    q1 = group['MonthlyCharges'].quantile(0.25)
    q3 = group['MonthlyCharges'].quantile(0.75)
    iqr = q3 - q1
    lower_bound = q1 - 1.5 * iqr
    upper_bound = q3 + 1.5 * iqr
    return pd.Series({'Lower Bound': lower_bound, 'Upper Bound': upper_bound})

bounds = agung.groupby('PaymentMethod').apply(calculate_bounds).reset_index()
print(bounds)

               PaymentMethod  Lower Bound  Upper Bound
0  Bank transfer (automatic)    -36.72500    170.77500
1    Credit card (automatic)    -39.61875    169.13125
2           Electronic check      8.32500    146.52500
3               Mailed check    -46.99375    132.05625

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\704640460.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  bounds = agung.groupby('PaymentMethod').apply(calculate_bounds).reset_index()

#histogram untuk melihat distribusi MonthlyCharges per masing-masing PaymentMethod
for method in agung['PaymentMethod'].unique():
    plt.figure(figsize=(8, 4))
    sns.histplot(agung[agung['PaymentMethod'] == method]['MonthlyCharges'], bins=30, kde=True)
    plt.title(f'Distribusi MonthlyCharges untuk {method}')
    plt.xlabel('MonthlyCharges')
    plt.ylabel('Frekuensi')
    plt.show()

agung.groupby('PaymentMethod')['MonthlyCharges'].describe()

#box plot
plt.figure(figsize=(6, 7))
sns.boxplot(x='PaymentMethod', y='MonthlyCharges', data=agung)
plt.title('Monthly Charges by Payment Method')
plt.xlabel('Payment Method')
plt.ylabel('Monthly Charges')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

agung.head()

agung.columns

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

agung['tenure'].describe()

count    7043.000000
mean       32.371149
std        24.559481
min         0.000000
25%         9.000000
50%        29.000000
75%        55.000000
max        72.000000
Name: tenure, dtype: float64

agung_tenure = agung.copy()

agung_tenure['TenureGroup'] = pd.cut(agung_tenure['tenure'], bins=[0, 20, 40, agung_tenure['tenure'].max()], labels=['Low', 'medium', 'high'], right=True, include_lowest=True)

tenure_counts = agung_tenure['TenureGroup'].value_counts().sort_index()

#jumlah pelanggan per kelompok tenure
tenure_counts

TenureGroup
Low       2878
medium    1408
high      2757
Name: count, dtype: int64

plt.figure(figsize=(7, 6))
sns.barplot(x=tenure_counts.index, y=tenure_counts.values)
plt.title('Jumlah Pelanggan Berdasarkan Kelompok Tenure', fontsize=14, fontweight='bold')
plt.xlabel('Kelompok Tenure', fontsize=10)
plt.ylabel('Jumlah Pelanggan', fontsize=10)
plt.tight_layout()
plt.show()

plt.figure(figsize=(7, 6))
ax = sns.barplot(x=tenure_counts.index, y=tenure_counts.values, hue=tenure_counts.index, palette='viridis')

for i, value in enumerate(tenure_counts.values):
    ax.text(i, value - 500 , str(value), ha='center', va='bottom', fontsize=12, fontweight='bold')

plt.title('Jumlah Pelanggan Berdasarkan Kelompok Tenure', fontsize=14, fontweight='bold')
plt.xlabel('Kelompok Tenure', fontsize=11)
plt.ylabel('Jumlah Pelanggan', fontsize=11)
plt.tight_layout()
plt.show()

agung.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

agung['SeniorCitizen'].unique()

array([0, 1])

agung['PhoneService'].unique()

array(['No', 'Yes'], dtype=object)

agung_mentah = agung.groupby(['SeniorCitizen', 'PhoneService'])['customerID'].nunique().reset_index(name='JumlahCustomerYangSeniorplusLanggananTelepon')
semua_customer = agung.groupby(['SeniorCitizen'])['customerID'].nunique().reset_index(name='SemuaCustomer')

hasil_rasio = agung_mentah.merge(semua_customer, on='SeniorCitizen', how='inner')

hasil_rasio['Persentase'] = round(100 * hasil_rasio['JumlahCustomerYangSeniorplusLanggananTelepon'] / hasil_rasio['SemuaCustomer'],2)

hasil_rasio

#visualisasi hasil_rasio
plt.figure(figsize=(6, 7))
sns.barplot(x='SeniorCitizen', y='Persentase', hue='PhoneService', data=hasil_rasio)
plt.title('Persentase Pelanggan Senior Citizen yang Berlangganan Layanan Telepon', fontweight='bold')
plt.xlabel('Senior Citizen')
plt.ylabel('Persentase (%)')
plt.xticks(rotation=0)

#kustom lokasi legends
plt.legend(title='Phone Service', loc='upper left')
plt.show()

#visualisasi hasil_rasio
plt.figure(figsize=(5, 6))
sns.barplot(x='SeniorCitizen', y='Persentase', hue='PhoneService', data=hasil_rasio, palette='viridis')
plt.title('Persentase Pelanggan Senior Citizen yang Berlangganan Layanan Telepon', fontsize=10, fontweight='bold')
plt.xlabel('Senior Citizen', fontsize=9)
plt.ylabel('Persentase (%)', fontsize=9)
plt.xticks(rotation=0)

#kustom lokasi legends dan judul
plt.legend(title='Phone Service', fontsize=8, loc='upper right', bbox_to_anchor=(1.3, 1), title_fontsize=9)
plt.show()

agung.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

agung['TotalCharges'].describe()

count     7043
unique    6531
top       20.2
freq        11
Name: TotalCharges, dtype: object

agung['TotalCharges'].head()

0      29.85
1     1889.5
2     108.15
3    1840.75
4     151.65
Name: TotalCharges, dtype: object

agung['TotalCharges'] = pd.to_numeric(agung['TotalCharges'], errors='coerce')

print(agung['TotalCharges'].isnull().sum())

11

agung.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

# histogram untuk melihat distribusi TotalCharges
plt.figure(figsize=(10, 6))
sns.histplot(agung['TotalCharges'], bins=50, kde=True)
plt.title('Distribusi Total Charges')
plt.xlabel('Total Charges')
plt.ylabel('Frekuensi')
plt.legend()
plt.tight_layout()
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\1844985906.py:7: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend()

#grafik kde plot untuk melihat distribusi TotalCharges
plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.tight_layout()
plt.show()

plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(agung['TotalCharges'].mean(), color='red', linestyle='--', label='Mean')
plt.axvline(agung['TotalCharges'].median(), color='green', linestyle='--', label='Median')
plt.tight_layout()
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
plt.show()

#garis q1  q3
q1 = agung['TotalCharges'].quantile(0.25)
q3 = agung['TotalCharges'].quantile(0.75)

plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(q1, color='blue', linestyle='--', label='Q1')
plt.axvline(q3, color='orange', linestyle='--', label='Q3')
plt.tight_layout()
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
plt.show()

#garis q1 q2 q3

plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(q1, color='blue', linestyle='--', label='Q1')
plt.axvline(q3, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1, q3, color='green', alpha=0.2, label=f'IQR Area (${q1:.0f} - ${q3:.0f})')
plt.tight_layout()
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
plt.show()

#mencari upperbound dan lowerbound
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
print(f"Lower Bound: {lower_bound:.2f}")
print(f"Upper Bound: {upper_bound:.2f}")

Lower Bound: -4688.48
Upper Bound: 8884.67

outliers = agung[agung['TotalCharges'] > upper_bound]
print("Jumlah outlier atas:", len(outliers))

Jumlah outlier atas: 0

#nilai iqr total charge
iqr = q3 - q1
print(f"IQR Total Charges: {iqr}")

IQR Total Charges: 3393.2875000000004

#upper bound dan lower bound
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
print(f"Lower Bound: {lower_bound}")
print(f"Upper Bound: {upper_bound}")

Lower Bound: -4688.481250000001
Upper Bound: 8884.66875

#garis upper bound
plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(q1, color='blue', linestyle='--', label='Q1')
plt.axvline(q3, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1, q3, color='green', alpha=0.2, label=f'IQR Area (${q1:.0f} - ${q3:.0f})')
plt.axvline(upper_bound, color='red', linestyle='--', label='Upper Bound')

plt.tight_layout()
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
plt.show()

# arsir gg
plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Distribusi Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(q1, color='blue', linestyle='--', label='Q1')
plt.axvline(q3, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1, q3, color='green', alpha=0.2, label=f'IQR Area (${q1:.0f} - ${q3:.0f})')
plt.axvline(upper_bound, color='red', linestyle='--', label='Upper Bound')
x_max = agung['TotalCharges'].max() + 2000
x_min = agung['TotalCharges'].min() - 2000
plt.xlim(x_min, x_max)
plt.axvspan(upper_bound, x_max, color='red', alpha=0.15, label='Area outlier (> 8885)')
plt.tight_layout()
plt.legend(loc='upper right', bbox_to_anchor=(1.28, 1), fontsize=8)
plt.grid(True, alpha=0.3)
plt.show()

# arsir gg
plt.figure(figsize=(8, 6))
sns.kdeplot(data=agung, x='TotalCharges', fill=True)
plt.title('Pola Biaya Pelanggan: Siapa yang Benar-Benar Menguntungkan?', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.axvline(q1, color='blue', linestyle='--', label='Q1')
plt.axvline(q3, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1, q3, color='green', alpha=0.2, label=f'Mainstream (Q1 - Q3)')
plt.axvline(upper_bound, color='red', linestyle='--', label='Upper Bound')
x_max = agung['TotalCharges'].max() + 2000
x_min = agung['TotalCharges'].min() - 2000
plt.xlim(x_min, x_max)
plt.axvspan(upper_bound, q3, color='purple', alpha=0.15, label='VIP Area (> Q3)')
plt.text((q1 + q3)/2, 0.00015, 'Mainstream\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text((q3 + upper_bound)/2, 0.00005, 'VIP\n(> Q3)', ha='center', fontsize=11, fontweight='bold',color='darkred',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.tight_layout()
plt.legend(loc='upper right', bbox_to_anchor=(1.28, 1), fontsize=8)
plt.grid(True, alpha=0.3)
plt.show()

#box plot 
plt.figure(figsize=(4, 6))
sns.boxplot(data=agung, y='TotalCharges')
plt.title('Box Plot Total Charges', fontsize=14, fontweight='bold')
plt.xlabel('Total Charges', fontsize=12)
plt.tight_layout()
plt.show()

agung['Churn'].describe()

count     7043
unique       2
top         No
freq      5174
Name: Churn, dtype: object

agung['Churn'].value_counts()

Churn
No     5174
Yes    1869
Name: count, dtype: int64

agung.groupby('Churn')['MonthlyCharges'].describe()

#histogram
plt.figure(figsize=(10, 6))
sns.histplot(data=agung, x='MonthlyCharges', hue='Churn', multiple='stack', kde=True, bins=30)
plt.title('Distribusi Monthly Charges berdasarkan Churn', fontsize=14, fontweight='bold')
plt.xlabel('Monthly Charges', fontsize=12)
plt.ylabel('Frekuensi', fontsize=12)
plt.legend(title='Churn', loc='upper right')
plt.tight_layout()
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\651463370.py:7: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  plt.legend(title='Churn', loc='upper right')

#kdeplot khusus churn
plt.figure(figsize=(10, 6))
sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, label='Churn = Yes', color='red')
sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, label='Churn = No', color='blue')
plt.title('Distribusi Monthly Charges berdasarkan Churn', fontsize=14, fontweight='bold')
plt.xlabel('Monthly Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.legend(title='Churn', loc='upper right')
plt.tight_layout()
plt.show()

# kde plot churn garis mean median
plt.figure(figsize=(10, 6))
sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, label='Churn = Yes', color='red')
sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, label='Churn = No', color='blue')
plt.axvline(agung[agung['Churn'] == 'Yes']['MonthlyCharges'].mean(), color='green', linestyle='--', label='Mean Churn = Yes')
plt.axvline(agung[agung['Churn'] == 'Yes']['MonthlyCharges'].median(), color='orange', linestyle='--', label='Median Churn = Yes')
plt.axvline(agung[agung['Churn'] == 'No']['MonthlyCharges'].mean(), color='red', linestyle='--', label='Mean Churn = No')
plt.axvline(agung[agung['Churn'] == 'No']['MonthlyCharges'].median(), color='blue', linestyle='--', label='Median Churn = No')
plt.title('Distribusi Monthly Charges berdasarkan Churn', fontsize=14, fontweight='bold')
plt.xlabel('Monthly Charges', fontsize=12)
plt.ylabel('Kepadatan', fontsize=12)
plt.legend(title='Churn', loc='upper right')
plt.tight_layout()
plt.grid(True, alpha=0.3)
plt.show()

#nilai iqr
q1_churn_yes = agung[agung['Churn'] == 'Yes']['MonthlyCharges'].quantile(0.25)
q3_churn_yes = agung[agung['Churn'] == 'Yes']['MonthlyCharges'].quantile(0.75)
iqr_churn_yes = q3_churn_yes - q1_churn_yes
q1_churn_no = agung[agung['Churn'] == 'No']['MonthlyCharges'].quantile(0.25)
q3_churn_no = agung[agung['Churn'] == 'No']['MonthlyCharges'].quantile(0.75)
iqr_churn_no = q3_churn_no - q1_churn_no
print(f"IQR Churn = Yes: {iqr_churn_yes}")
print(f"IQR Churn = No: {iqr_churn_no}")

IQR Churn = Yes: 38.050000000000004
IQR Churn = No: 63.300000000000004

# upper bound dan lower bound
lower_bound_churn_yes = q1_churn_yes - 1.5 * iqr_churn_yes
upper_bound_churn_yes = q3_churn_yes + 1.5 * iqr_churn_yes
lower_bound_churn_no = q1_churn_no - 1.5 * iqr_churn_no
upper_bound_churn_no = q3_churn_no + 1.5 * iqr_churn_no
print(f"Lower Bound Churn = Yes: {lower_bound_churn_yes}")
print(f"Upper Bound Churn = Yes: {upper_bound_churn_yes}")
print(f"Lower Bound Churn = No: {lower_bound_churn_no}")
print(f"Upper Bound Churn = No: {upper_bound_churn_no}")

Lower Bound Churn = Yes: -0.9250000000000043
Upper Bound Churn = Yes: 151.275
Lower Bound Churn = No: -69.85
Upper Bound Churn = No: 183.35000000000002

fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharey=True)


sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0],color='red')
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')


sns.kdeplot(data=agung[agung['Churn'] == 'No'],x='MonthlyCharges',fill=True,ax=axes[1],color='blue')
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

fig.suptitle('Distribusi Monthly Charges Berdasarkan Status Churn', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

fig, axes = plt.subplots(1, 2, figsize=(13, 6), sharey=True)

sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0], color='red')
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')

sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, ax=axes[1], color='blue')
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

axes[0].axvline(q1_churn_yes, color='blue', linestyle='--', label='Q1')
axes[0].axvline(q3_churn_yes, color='orange', linestyle='--', label='Q3')
axes[0].axvline(upper_bound_churn_yes, color='red', linestyle='--', label='Upper Bound')
axes[1].axvline(q1_churn_no, color='blue', linestyle='--', label='Q1')
axes[1].axvline(q3_churn_no, color='orange', linestyle='--', label='Q3')
axes[1].axvline(upper_bound_churn_no, color='red', linestyle='--', label='Upper Bound')

fig.suptitle('Distribusi Monthly Charges Berdasarkan Status Churn', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.legend(loc='upper right',  bbox_to_anchor=(1.30, 1), fontsize=10)
plt.show()

# arsir area iqr dan outlier
fig, axes = plt.subplots(1, 2, figsize=(13, 6), sharey=True)

sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0], color='red', alpha=0.7)
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')

sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, ax=axes[1], color='blue', alpha=0.7)
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

axes[0].axvline(q1_churn_yes, color='green', linestyle='--', label='Q1')
axes[0].axvline(q3_churn_yes, color='orange', linestyle='--', label='Q3')
axes[0].axvline(upper_bound_churn_yes, color='black', linestyle='--', label='Upper Bound')
axes[1].axvline(q1_churn_no, color='green', linestyle='--', label='Q1')
axes[1].axvline(q3_churn_no, color='orange', linestyle='--', label='Q3')
axes[1].axvline(upper_bound_churn_no, color='black', linestyle='--', label='Upper Bound')

x_max_bulanan = agung['MonthlyCharges'].max() + 80
x_min_bulanan = agung['MonthlyCharges'].min() - 20
axes[0].set_xlim(x_min_bulanan, x_max_bulanan)
axes[1].set_xlim(x_min_bulanan, x_max_bulanan)
axes[0].axvspan(q1_churn_yes, q3_churn_yes, color='green', alpha=0.2, label='IQR Area (Q1-Q3)')
axes[1].axvspan(q1_churn_no, q3_churn_no, color='green', alpha=0.2, label='IQR Area (Q1-Q3)')
axes[0].axvspan(upper_bound_churn_yes, x_max_bulanan, color='red', alpha=0.15, label='Outlier Area (> Upper Bound)')
axes[1].axvspan(upper_bound_churn_no, x_max_bulanan, color='red', alpha=0.15, label='Outlier Area (> Upper Bound)')
fig.suptitle('Distribusi Monthly Charges Berdasarkan Status Churn', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.legend(loc='upper right', bbox_to_anchor=(1.30, 1), fontsize=10)
plt.show()

# gacorr
fig, axes = plt.subplots(1, 2, figsize=(13, 6), sharey=True)

sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0], color='red', alpha=0.7)
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')

sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, ax=axes[1], color='blue', alpha=0.7)
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

axes[0].axvline(q1_churn_yes, color='green', linestyle='--', label='Q1')
axes[0].axvline(q3_churn_yes, color='orange', linestyle='--', label='Q3')
axes[0].axvline(upper_bound_churn_yes, color='black', linestyle='--', label='Upper Bound')
axes[1].axvline(q1_churn_no, color='green', linestyle='--', label='Q1')
axes[1].axvline(q3_churn_no, color='orange', linestyle='--', label='Q3')
axes[1].axvline(upper_bound_churn_no, color='black', linestyle='--', label='Upper Bound')

x_max_bulanan = agung['MonthlyCharges'].max() + 75
x_min_bulanan = agung['MonthlyCharges'].min() - 20

axes[0].set_xlim(x_min_bulanan, x_max_bulanan)
axes[1].set_xlim(x_min_bulanan, x_max_bulanan)
axes[0].axvspan(q1_churn_yes, q3_churn_yes, color='yellow', alpha=0.2, label='Low Paying Churners (kikir) Area (Q1-Q3)')
axes[1].axvspan(q1_churn_no, q3_churn_no, color='red', alpha=0.2, label='Low Spending Loyal Area (Q1-Q3)')
axes[0].axvspan(upper_bound_churn_yes, q3_churn_yes, color='green', alpha=0.15, label='Critical Risk Area (> Q3)')
axes[1].axvspan(upper_bound_churn_no, q3_churn_no, color='black', alpha=0.15, label='Resilient Budget Area (> Q3)')

axes[0].text(q1_churn_yes + 19, 0.012, 'Low Paying \n Churners (kikir)', ha='center', fontsize=8, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
axes[0].text(q3_churn_yes + 25, 0.017, 'Critical Risk', ha='center', fontsize=8, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[1].text(q1_churn_no + 28, 0.012, 'Low Spending Loyal', ha='center', fontsize=8, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
axes[1].text(q3_churn_no + 30, 0.010, 'Resilient Budget', ha='center', fontsize=8, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
fig.suptitle('Distribusi Monthly Charges Berdasarkan Status Churn', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.legend(loc='upper right', bbox_to_anchor=(1.30, 1), fontsize=10)
plt.show()

# gacorr bet
fig, axes = plt.subplots(1, 2, figsize=(13, 6), sharey=True)

sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0], color='red', alpha=0.7)
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')

sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, ax=axes[1], color='blue', alpha=0.7)
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

axes[0].axvline(q1_churn_yes, color='green', linestyle='--', label='Q1')
axes[0].axvline(q3_churn_yes, color='orange', linestyle='--', label='Q3')
axes[0].axvline(upper_bound_churn_yes, color='black', linestyle='--', label='Upper Bound')
axes[1].axvline(q1_churn_no, color='green', linestyle='--', label='Q1')
axes[1].axvline(q3_churn_no, color='orange', linestyle='--', label='Q3')
axes[1].axvline(upper_bound_churn_no, color='black', linestyle='--', label='Upper Bound')

x_max_bulanan = agung['MonthlyCharges'].max() + 75
x_min_bulanan = agung['MonthlyCharges'].min() - 20
axes[0].set_xlim(x_min_bulanan, x_max_bulanan)
axes[1].set_xlim(x_min_bulanan, x_max_bulanan)

axes[0].axvspan(q1_churn_yes, q3_churn_yes, color='yellow', alpha=0.2, label='Low Paying Churners (Q1–Q3)')
axes[1].axvspan(q1_churn_no, q3_churn_no, color='red', alpha=0.2, label='Low Spending Loyal (Q1–Q3)')
axes[0].axvspan(upper_bound_churn_yes, q3_churn_yes, color='green', alpha=0.15, label='Critical Risk Area (> Q3)')
axes[1].axvspan(upper_bound_churn_no, q3_churn_no, color='black', alpha=0.15, label='Resilient Budget Area (> Q3)')

axes[0].text(q1_churn_yes + 19, 0.012, 'Low Paying \n Churners (kikir)', ha='center', fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
axes[0].text(q3_churn_yes + 25, 0.017, 'Critical Risk', ha='center', fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
axes[1].text(q1_churn_no + 28, 0.012, 'Low Spending Loyal', ha='center', fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
axes[1].text(q3_churn_no + 30, 0.010, 'Resilient Budget', ha='center', fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

handles_0, labels_0 = axes[0].get_legend_handles_labels()
handles_1, labels_1 = axes[1].get_legend_handles_labels()

all_handles = handles_0 + handles_1
all_labels = labels_0 + labels_1

unique = dict()
for h, l in zip(all_handles, all_labels):
    if l not in unique:
        unique[l] = h


fig.legend(unique.values(), unique.keys(), loc='upper right', bbox_to_anchor=(1.2, 1), fontsize=10)

fig.suptitle('Distribusi Monthly Charges Berdasarkan Status Churn', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

fig, axes = plt.subplots(1, 2, figsize=(13, 6), sharey=True)

sns.kdeplot(data=agung[agung['Churn'] == 'Yes'], x='MonthlyCharges', fill=True, ax=axes[0], color='#7B1FA2', alpha=0.7)
axes[0].set_title('Churn = Yes', fontsize=12, fontweight='bold')
axes[0].set_xlabel('Monthly Charges')
axes[0].set_ylabel('Kepadatan')

sns.kdeplot(data=agung[agung['Churn'] == 'No'], x='MonthlyCharges', fill=True, ax=axes[1], color='#00796B', alpha=0.7)
axes[1].set_title('Churn = No', fontsize=12, fontweight='bold')
axes[1].set_xlabel('Monthly Charges')
axes[1].set_ylabel('')

axes[0].axvline(q1_churn_yes, color='#388E3C', linestyle='--', label='Q1')
axes[0].axvline(q3_churn_yes, color='#F57C00', linestyle='--', label='Q3')
axes[0].axvline(upper_bound_churn_yes, color='#424242', linestyle='--', label='Upper Bound')
axes[1].axvline(q1_churn_no, color='#388E3C', linestyle='--', label='Q1')
axes[1].axvline(q3_churn_no, color='#F57C00', linestyle='--', label='Q3')
axes[1].axvline(upper_bound_churn_no, color='#424242', linestyle='--', label='Upper Bound')

x_max_bulanan = agung['MonthlyCharges'].max() + 75
x_min_bulanan = agung['MonthlyCharges'].min() - 20
axes[0].set_xlim(x_min_bulanan, x_max_bulanan)
axes[1].set_xlim(x_min_bulanan, x_max_bulanan)

axes[0].axvspan(q1_churn_yes, q3_churn_yes, color='#FFF59D', alpha=0.5, label='Low Paying Churners (Q1–Q3)')
axes[0].axvspan(upper_bound_churn_yes, q3_churn_yes, color='#E1BEE7', alpha=0.4, label='Critical Risk Area (> Q3)')
axes[1].axvspan(q1_churn_no, q3_churn_no, color='#B2DFDB', alpha=0.5, label='Low Spending Loyal (Q1–Q3)')
axes[1].axvspan(upper_bound_churn_no, q3_churn_no, color='#E0E0E0', alpha=0.5, label='Resilient Budget Area (> Q3)')

axes[0].axvspan(x_min_bulanan, q1_churn_yes, color='#FFCDD2', alpha=0.4, label='Im Here for free trial (< Q1)')
axes[1].axvspan(x_min_bulanan, q1_churn_no, color='#C5CAE9', alpha=0.4, label='Promising (< Q1)')



axes[0].text(q1_churn_yes + 19, 0.012, 'Low Paying \nChurners (kikir)\n(Q1–Q3)', ha='center',fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[0].text(q3_churn_yes + 25, 0.017, 'Critical Risk Area\n(> Q3)', ha='center',fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[1].text(q1_churn_no + 28, 0.012, 'Low Spending Loyal\n(Q1–Q3)', ha='center',fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[1].text(q3_churn_no + 30, 0.010, 'Resilient Budget Area\n(> Q3)', ha='center',fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[0].text(q1_churn_yes - 20, 0.005, 'Im Here For\nFree Trial\n(< Q1)', ha='center', fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

axes[1].text(q1_churn_no - 10, 0.005, 'Promising\n(< Q1)', ha='center',fontsize=8, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))


handles_0, labels_0 = axes[0].get_legend_handles_labels()
handles_1, labels_1 = axes[1].get_legend_handles_labels()
all_handles = handles_0 + handles_1
all_labels = labels_0 + labels_1
unique = dict()
for h, l in zip(all_handles, all_labels):
    if l not in unique:
        unique[l] = h

fig.legend(unique.values(), unique.keys(), loc='upper right', bbox_to_anchor=(1.20, 1), fontsize=10)

# Judul keseluruhan
fig.suptitle('Siapa yang Akan Pergi, dan Siapa yang Layak Dipertahankan?', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

#boxplot
plt.figure(figsize=(8, 6))
sns.boxplot(data=agung, x='Churn', y='MonthlyCharges')
plt.title('Box Plot Monthly Charges berdasarkan Churn', fontsize=14, fontweight='bold')
plt.xlabel('Churn', fontsize=12)
plt.ylabel('Monthly Charges', fontsize=12)
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

from scipy.stats import mannwhitneyu

churn_yes = agung[agung['Churn'] == 'Yes']['MonthlyCharges']
churn_no = agung[agung['Churn'] == 'No']['MonthlyCharges']

# Lakukan uji Mann-Whitney U Test
stat, p = mannwhitneyu(churn_yes, churn_no, alternative='two-sided')

# Tampilkan hasil
print("Statistik U:", stat)
print("p-value:", p)

Statistik U: 6003125.5
p-value: 3.311627651988584e-54

alpha = 0.05
if p < alpha:
    print("Hasil: Tolak H0 → Terdapat perbedaan signifikan antara biaya bulanan pelanggan churn dan tidak churn.")
else:
    print("Hasil: Gagal tolak H0 → Tidak terdapat perbedaan signifikan antara biaya bulanan pelanggan churn dan tidak churn.")

Hasil: Tolak H0 → Terdapat perbedaan signifikan antara biaya bulanan pelanggan churn dan tidak churn.

agung.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

agung['PartnerDependents'] = agung['Partner'].astype(str) + '_' + agung['Dependents'].astype(str)

hasil_partner_dependents = agung['PartnerDependents'].value_counts().reset_index()
hasil_partner_dependents.columns = ['PartnerDependents', 'JumlahPelanggan']

print(hasil_partner_dependents)

rasio_churn_partner_dependents = agung.groupby('PartnerDependents')['Churn'].value_counts(normalize=True).unstack().fillna(0)

print(rasio_churn_partner_dependents)

  PartnerDependents  JumlahPelanggan
0             No_No             3280
1           Yes_Yes             1749
2            Yes_No             1653
3            No_Yes              361
Churn                    No       Yes
PartnerDependents                    
No_No              0.657622  0.342378
No_Yes             0.786704  0.213296
Yes_No             0.745917  0.254083
Yes_Yes            0.857633  0.142367

rasio_churn_partner_dependents = rasio_churn_partner_dependents * 100
print(rasio_churn_partner_dependents)

Churn                     No        Yes
PartnerDependents                      
No_No              65.762195  34.237805
No_Yes             78.670360  21.329640
Yes_No             74.591652  25.408348
Yes_Yes            85.763293  14.236707

label_map = {
    'No_No': 'Tidak Ada Keduanya',
    'No_Yes': 'Tanggungan Saja',
    'Yes_No': 'Pasangan Saja',
    'Yes_Yes': 'Pasangan & Tanggungan'
}

rasio_churn_partner_dependents.rename(index=label_map, inplace=True)

rasio_churn_partner_dependents.plot(kind='bar',stacked=False,figsize=(10, 6))

plt.title('Rasio Churn dan Tidak Churn berdasarkan Kombinasi Partner & Dependents')
plt.xlabel('Kombinasi Partner & Dependents')
plt.ylabel('Rasio (%)')
plt.xticks(rotation=15)
plt.legend(title='Status Churn')

plt.tight_layout()
plt.show()

rasio_churn_partner_dependents.plot(kind='bar',stacked=False,color=['salmon', 'skyblue'],edgecolor='black',figsize=(10, 6))

plt.title('Rasio Churn dan Tidak Churn berdasarkan Kombinasi Partner & Dependents')
plt.xlabel('Kombinasi Partner & Dependents')
plt.ylabel('Rasio (%)')
plt.xticks(rotation=15)
plt.legend(title='Status Churn')
plt.grid(alpha=0.3)


plt.tight_layout()
plt.show()

agung['Status'] = agung.apply(lambda row: 'Married' if (row['SeniorCitizen'] == 1 or row['Partner'] == 'Yes' or row['Dependents'] == 'Yes') else 'Single', axis=1)

profil_pelanggan = agung.groupby(['Contract', 'Status', 'Churn']).agg({
    'tenure': 'mean',
    'MonthlyCharges': 'mean',
    'customerID': 'count'
}).reset_index().rename(columns={'customerID': 'JumlahPelanggan', 'tenure': 'RataRataTenure', 'MonthlyCharges': 'RataRataMonthlyCharges'})

# Visualisasi hubungan antara kontrak, status, churn, rata-rata tenure, dan rata-rata biaya bulanan

fig, axes = plt.subplots(2, 1, figsize=(14, 12))

# Plot 1: Rata-rata tenure pelanggan berdasarkan kontrak, status, dan churn
sns.barplot(data=profil_pelanggan, x='Contract', y='RataRataTenure', hue='Churn', palette='coolwarm', ax=axes[0])
axes[0].set_title('Rata-rata Durasi Berlangganan (Tenure) Berdasarkan Jenis Kontrak dan Churn')
axes[0].set_ylabel('Rata-rata Tenure (Bulan)')
axes[0].set_xlabel('Jenis Kontrak')
axes[0].legend(title='Churn')

# Plot 2: Rata-rata biaya bulanan berdasarkan kontrak, status, dan churn
sns.barplot(data=profil_pelanggan, x='Contract', y='RataRataMonthlyCharges', hue='Churn', palette='coolwarm', ax=axes[1])
axes[1].set_title('Rata-rata Biaya Bulanan Berdasarkan Jenis Kontrak dan Churn')
axes[1].set_ylabel('Rata-rata Biaya Bulanan ($)')
axes[1].set_xlabel('Jenis Kontrak')
axes[1].legend(title='Churn')

# Menambahkan judul utama dan storyline
fig.suptitle('Storytelling Profil Pelanggan Telco: Hubungan antara Jenis Kontrak, Status, Durasi Berlangganan, dan Biaya Bulanan terhadap Churn', fontsize=16)

plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

# Visualisasi tambahan yang melibatkan status pelanggan (Married/Single) untuk analisis lebih mendalam

fig, axes = plt.subplots(2, 1, figsize=(14, 12))

# Plot 1: Rata-rata tenure berdasarkan jenis kontrak, status, dan churn
sns.barplot(data=profil_pelanggan, x='Contract', y='RataRataTenure', hue='Status', palette='Set2', ax=axes[0])
axes[0].set_title('Rata-rata Durasi Berlangganan Berdasarkan Jenis Kontrak dan Status Pelanggan')
axes[0].set_ylabel('Rata-rata Tenure (Bulan)')
axes[0].set_xlabel('Jenis Kontrak')
axes[0].legend(title='Status')

# Plot 2: Rata-rata biaya bulanan berdasarkan jenis kontrak, status, dan churn
sns.barplot(data=profil_pelanggan, x='Contract', y='RataRataMonthlyCharges', hue='Status', palette='Set2', ax=axes[1])
axes[1].set_title('Rata-rata Biaya Bulanan Berdasarkan Jenis Kontrak dan Status Pelanggan')
axes[1].set_ylabel('Rata-rata Biaya Bulanan ($)')
axes[1].set_xlabel('Jenis Kontrak')
axes[1].legend(title='Status')

# Menambahkan storyline
fig.suptitle('Analisis Mendalam: Profil Pelanggan Berdasarkan Jenis Kontrak, Status, Tenure, dan Biaya Bulanan', fontsize=16)

plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

# Menyiapkan data pelanggan yang tidak churn untuk analisis faktor yang mempengaruhi pelanggan loyal
no_churn = agung[agung['Churn'] == 'No']

# Plot 1: Distribusi tenure untuk pelanggan yang tidak churn
plt.figure(figsize=(14, 6))
sns.histplot(no_churn['tenure'], bins=30, kde=True)
plt.title('Distribusi Tenure untuk Pelanggan yang Tidak Churn')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

# Plot 2: Perbandingan jenis kontrak untuk pelanggan tidak churn
plt.figure(figsize=(8, 6))
sns.countplot(data=no_churn, x='Contract', palette='Set2')
plt.title('Jenis Kontrak Pelanggan yang Tidak Churn')
plt.xlabel('Jenis Kontrak')
plt.ylabel('Jumlah Pelanggan')
plt.show()

# Plot 3: Status pelanggan yang tidak churn
plt.figure(figsize=(8, 6))
sns.countplot(data=no_churn, x='Status', palette='Set1')
plt.title('Status Pelanggan yang Tidak Churn')
plt.xlabel('Status Pelanggan')
plt.ylabel('Jumlah Pelanggan')
plt.show()

# Plot 4: Distribusi Monthly Charges untuk pelanggan tidak churn
plt.figure(figsize=(14, 6))
sns.histplot(no_churn['MonthlyCharges'], bins=30, kde=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\4052751098.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Contract', palette='Set2')

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\4052751098.py:22: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Status', palette='Set1')

# Mengecek apakah ada pelanggan yang memenuhi semua kriteria ideal secara bersamaan
ideal_customer = agung[
    (agung['tenure'] > 24) & 
    (agung['Contract'] == 'Month-to-month') & 
    (agung['Status'] == 'Married') & 
    (agung['MonthlyCharges'].between(40, 80))
]

# Menghitung jumlah pelanggan dengan status tersebut berdasarkan churn
status_ideal_customer = ideal_customer['Churn'].value_counts().reset_index()
status_ideal_customer.columns = ['Churn', 'JumlahPelanggan']
status_ideal_customer

# Visualisasi piechart jumlah pelanggan ideal berdasarkan status churn
plt.figure(figsize=(8, 6))
plt.pie(status_ideal_customer['JumlahPelanggan'], labels=status_ideal_customer['Churn'], autopct='%1.1f%%', startangle=140, colors=['#4CAF50', '#FF5722'])
plt.title('Jumlah Pelanggan Ideal berdasarkan Status Churn')
plt.axis('equal')  # Equal aspect ratio ensures that pie chart is circular.
plt.tight_layout()
plt.show()

rank2 = agung[
    (agung['Contract'] == 'Month-to-month') &
    (agung['Status'] == 'Married') &
    (agung['tenure'] <= 12) &
    (agung['MonthlyCharges'].between(70, 100))
]

rank2 = rank2['Churn'].value_counts().reset_index()
rank2.columns = ['Churn', 'JumlahPelanggan']
rank2

#pie chart
plt.figure(figsize=(8, 6))
plt.pie(rank2['JumlahPelanggan'], labels=rank2['Churn'], autopct='%1.1f%%', startangle=140, colors=['#2196F3', '#FFC107'])
plt.title('Jumlah Pelanggan Berdasarkan Kriteria Rank 2')
plt.axis('equal')  
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

# Membuat subplots 2x2
fig, axes = plt.subplots(2, 2, figsize=(16, 12))

# Plot 1: Distribusi tenure
sns.histplot(no_churn['tenure'], bins=30, kde=True, ax=axes[0, 0])
axes[0, 0].set_title('Distribusi Tenure untuk Pelanggan yang Tidak Churn')
axes[0, 0].set_xlabel('Tenure (bulan)')
axes[0, 0].set_ylabel('Jumlah Pelanggan')

# Plot 2: Jenis kontrak
sns.countplot(data=no_churn, x='Contract', palette='Set2', ax=axes[0, 1])
axes[0, 1].set_title('Jenis Kontrak Pelanggan yang Tidak Churn')
axes[0, 1].set_xlabel('Jenis Kontrak')
axes[0, 1].set_ylabel('Jumlah Pelanggan')

# Plot 3: Status pelanggan
sns.countplot(data=no_churn, x='Status', palette='Set1', ax=axes[1, 0])
axes[1, 0].set_title('Status Pelanggan yang Tidak Churn')
axes[1, 0].set_xlabel('Status Pelanggan')
axes[1, 0].set_ylabel('Jumlah Pelanggan')

# Plot 4: Distribusi biaya bulanan
sns.histplot(no_churn['MonthlyCharges'], bins=30, kde=True, color='green', ax=axes[1, 1])
axes[1, 1].set_title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn')
axes[1, 1].set_xlabel('Monthly Charges ($)')
axes[1, 1].set_ylabel('Jumlah Pelanggan')

# Menambahkan judul utama
fig.suptitle('Analisis Pelanggan yang Tidak Churn: Durasi, Kontrak, Status, dan Biaya Bulanan', fontsize=16)

# Mengatur layout agar rapi
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3627317045.py:13: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Contract', palette='Set2', ax=axes[0, 1])
C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3627317045.py:19: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Status', palette='Set1', ax=axes[1, 0])

# Plot 1: Distribusi tenure untuk pelanggan yang tidak churn
plt.figure(figsize=(14, 6))
sns.histplot(no_churn['tenure'], bins=30, kde=True)
plt.title('Distribusi Tenure untuk Pelanggan yang Tidak Churn')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

# Plot 2: garis IQR , q1, q3, upper bound dan lower bound untuk plot distribusi tenure pelanggan tidak churn
q1_tenure_no_churn = no_churn['tenure'].quantile(0.25)
q3_tenure_no_churn = no_churn['tenure'].quantile(0.75)
iqr_tenure_no_churn = q3_tenure_no_churn - q1_tenure_no_churn
lower_bound_tenure_no_churn = q1_tenure_no_churn - 1.5 * iqr_tenure_no_churn
upper_bound_tenure_no_churn = q3_tenure_no_churn + 1.5 * iqr_tenure_no_churn

plt.figure(figsize=(14, 6))
sns.histplot(no_churn['tenure'], bins=30, kde=True)
plt.axvline(q1_tenure_no_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_tenure_no_churn, color='orange', linestyle='--', label='Q3')
plt.title('Distribusi Tenure untuk Pelanggan yang Tidak Churn dengan IQR')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Jumlah Pelanggan')
plt.legend()
plt.show()

q1_tenure_no_churn = no_churn['tenure'].quantile(0.25)
q3_tenure_no_churn = no_churn['tenure'].quantile(0.75)
iqr_tenure_no_churn = q3_tenure_no_churn - q1_tenure_no_churn
lower_bound_tenure_no_churn = q1_tenure_no_churn - 1.5 * iqr_tenure_no_churn
upper_bound_tenure_no_churn = q3_tenure_no_churn + 1.5 * iqr_tenure_no_churn

q3_tenure_no_churn

np.float64(61.0)

# Plot distribusi tenure pelanggan tidak churn menggunakan KDE plot dengan garis Q1, Q3, IQR
plt.figure(figsize=(14, 6))

# KDE plot
sns.kdeplot(no_churn['tenure'], fill=True)

# Garis Q1, Q3, Lower Bound dan Upper Bound
plt.axvline(q1_tenure_no_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_tenure_no_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_tenure_no_churn, q3_tenure_no_churn, color='green', alpha=0.2, label='IQR Area (Q1-Q3)')

# Label dan judul
plt.title('Distribusi Tenure Pelanggan Tidak Churn (KDE) dengan IQR')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Kepadatan')
plt.legend()
plt.show()

plt.figure(figsize=(14, 6))


sns.kdeplot(no_churn['tenure'], fill=True)


plt.axvline(q1_tenure_no_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_tenure_no_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_tenure_no_churn, q3_tenure_no_churn, color='red', alpha=0.2, label='IQR Area (Q1-Q3)')
x_min_mantap = q1_tenure_no_churn - 20
x_max_mantap = q3_tenure_no_churn + 20
plt.xlim(x_min_mantap, x_max_mantap)
plt.axvspan(q3_tenure_no_churn, x_max_mantap, color='blue', alpha=0.2, label='IQR Area (Q1-Q3)')
plt.axvspan(q1_tenure_no_churn, x_min_mantap, color='yellow', alpha=0.2, label='IQR Area (Q1-Q3)')

plt.text(q1_tenure_no_churn - 10, 0.008, 'Safe\n(<Q1)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q3_tenure_no_churn + 10 , 0.008, 'Most Loyal\nGroup(> Q3)', ha='center', fontsize=11, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q1_tenure_no_churn + 20, 0.008, 'Low Loyal\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold',color = 'darkred', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

plt.title('Distribusi Tenure Pelanggan Tidak Churn (KDE) dengan IQR')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Kepadatan')
plt.legend()
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(data=no_churn, x='Contract', palette='Set2')

plt.title('Jenis Kontrak Pelanggan yang Tidak Churn')
plt.xlabel('Jenis Kontrak')
plt.ylabel('Jumlah Pelanggan')
ax = plt.gca()
ax.annotate(
    'Pelanggan yang tidak churn\npaling banyak pada kontrak bulanan',  
    xy=(0.4, 2000),  
    xytext=(1.2, 1900),  
    arrowprops=dict(facecolor='steelblue', arrowstyle='->'),
    ha='center'
)
plt.tight_layout()
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\669239375.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Contract', palette='Set2')

# Plot 3: Status pelanggan yang tidak churn
plt.figure(figsize=(8, 6))
sns.countplot(data=no_churn, x='Status', palette='Set1')
plt.title('Status Pelanggan yang Tidak Churn')
plt.xlabel('Status Pelanggan')
plt.ylabel('Jumlah Pelanggan')
ax = plt.gca()
ax.annotate(
    'Pelanggan yang paling banyak tidak churn\nmerupakan pelanggan yang memiliki \nstatus "married"',  
    xy=(0.4, 3000),  
    xytext=(1, 2800),  
    arrowprops=dict(facecolor='steelblue', arrowstyle='->'),
    ha='center'
)
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3295203990.py:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=no_churn, x='Status', palette='Set1')

# Plot 4: Distribusi Monthly Charges untuk pelanggan tidak churn
plt.figure(figsize=(14, 6))
sns.histplot(no_churn['MonthlyCharges'], bins=30, kde=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

plt.figure(figsize=(14, 6))
sns.kdeplot(data=no_churn['MonthlyCharges'], fill=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn (KDE Plot)')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Kepadatan')
plt.show()

q1_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.25)
q3_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.75)
iqr_monthly_charges_no_churn = q3_monthly_charges_no_churn - q1_monthly_charges_no_churn
lower_bound_monthly_charges_no_churn = q1_monthly_charges_no_churn - 1.5 * iqr_monthly_charges_no_churn
upper_bound_monthly_charges_no_churn = q3_monthly_charges_no_churn + 1.5 * iqr_monthly_charges_no_churn

plt.figure(figsize=(14, 6))
sns.kdeplot(data=no_churn['MonthlyCharges'], fill=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn (KDE Plot)')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Kepadatan')

x_min_3 = q1_monthly_charges_no_churn - 30
x_max_3= q3_monthly_charges_no_churn + 50
plt.xlim(x_min_3, x_max_3)

plt.axvline(q1_monthly_charges_no_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_monthly_charges_no_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_monthly_charges_no_churn, q3_monthly_charges_no_churn, color='yellow', alpha=0.2, label='IQR Area (Q1-Q3)')
# plt.axvspan(lower_bound_monthly_charges_no_churn, q1_monthly_charges_no_churn, color='red', alpha=0.2, label='Lower Bound Area (< Q1)')
# plt.axvspan(q3_monthly_charges_no_churn, upper_bound_monthly_charges_no_churn, color='blue', alpha=0.2, label='Upper Bound Area (> Q3)')
# plt.axvspan(upper_bound_monthly_charges_no_churn, no_churn['MonthlyCharges'].max(), color='green', alpha=0.2, label='Upper Bound Area (> Q3)')
plt.text(q1_monthly_charges_no_churn + 10, 0.008, 'Safe\n(<Q1)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q3_monthly_charges_no_churn + 10, 0.008, 'Safe\n(> Q3)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q1_monthly_charges_no_churn + 20, 0.008, 'Churner\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold', color='darkred', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.legend()
plt.show()

q1_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.25)
q3_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.75)
iqr_monthly_charges_no_churn = q3_monthly_charges_no_churn - q1_monthly_charges_no_churn
lower_bound_monthly_charges_no_churn = q1_monthly_charges_no_churn - 1.5 * iqr_monthly_charges_no_churn
upper_bound_monthly_charges_no_churn = q3_monthly_charges_no_churn + 1.5 * iqr_monthly_charges_no_churn
q3_monthly_charges_no_churn

np.float64(88.4)

q1_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.25)
q3_monthly_charges_no_churn = no_churn['MonthlyCharges'].quantile(0.75)
iqr_monthly_charges_no_churn = q3_monthly_charges_no_churn - q1_monthly_charges_no_churn
lower_bound_monthly_charges_no_churn = q1_monthly_charges_no_churn - 1.5 * iqr_monthly_charges_no_churn
upper_bound_monthly_charges_no_churn = q3_monthly_charges_no_churn + 1.5 * iqr_monthly_charges_no_churn

plt.figure(figsize=(14, 6))
sns.kdeplot(data=no_churn['MonthlyCharges'], fill=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn (KDE Plot)')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Kepadatan')

x_min_3 = q1_monthly_charges_no_churn - 30
x_max_3= q3_monthly_charges_no_churn + 50
plt.xlim(x_min_3, x_max_3)

plt.axvline(q1_monthly_charges_no_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_monthly_charges_no_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_monthly_charges_no_churn, q3_monthly_charges_no_churn, color='red', alpha=0.2, label='IQR Area (Q1-Q3)')
plt.axvspan(x_min_3, q1_monthly_charges_no_churn, color='blue', alpha=0.2, label='Safe Area (< Q1)')
plt.axvspan(q3_monthly_charges_no_churn, x_max_3, color='yellow', alpha=0.2, label='Safe Area (> Q3)')
# plt.axvspan(upper_bound_monthly_charges_no_churn, no_churn['MonthlyCharges'].max(), color='green', alpha=0.2, label='Upper Bound Area (> Q3)')
plt.text(q1_monthly_charges_no_churn -10, 0.008, 'Highest Amount\n(<Q1)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q3_monthly_charges_no_churn + 20, 0.008, 'Safe\n(> Q3)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q1_monthly_charges_no_churn + 30, 0.008, 'Low\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold', color='darkred', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.legend()
plt.show()

yes_churn = agung[agung['Churn'] == 'Yes']

plt.figure(figsize=(14, 6))
sns.histplot(yes_churn['tenure'], bins=30, kde=True)
plt.title('Distribusi Tenure untuk Pelanggan yang Churn')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(data=yes_churn, x='Contract', palette='Set2')
plt.title('Jenis Kontrak Pelanggan yang Churn')
plt.xlabel('Jenis Kontrak')
plt.ylabel('Jumlah Pelanggan')
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(data=yes_churn, x='Status', palette='Set1')
plt.title('Status Pelanggan yang Churn')
plt.xlabel('Status Pelanggan')
plt.ylabel('Jumlah Pelanggan')
plt.show()

plt.figure(figsize=(14, 6))
sns.histplot(yes_churn['MonthlyCharges'], bins=30, kde=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Churn')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Jumlah Pelanggan')
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3745456733.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=yes_churn, x='Contract', palette='Set2')

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3745456733.py:22: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=yes_churn, x='Status', palette='Set1')

#cari q1_tenure_yes_churn , q3_tenure_yes_churn 
q1_tenure_yes_churn = yes_churn['tenure'].quantile(0.25)
q3_tenure_yes_churn = yes_churn['tenure'].quantile(0.75)
iqr_tenure_yes_churn = q3_tenure_yes_churn - q1_tenure_yes_churn
lower_bound_tenure_yes_churn = q1_tenure_yes_churn - 1.5 * iqr_tenure_yes_churn
upper_bound_tenure_yes_churn = q3_tenure_yes_churn + 1.5 * iqr_tenure_yes_churn

plt.figure(figsize=(14, 6))


sns.kdeplot(yes_churn['tenure'], fill=True)


plt.axvline(q1_tenure_yes_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_tenure_yes_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_tenure_yes_churn, q3_tenure_yes_churn, color='blue', alpha=0.2, label='IQR Area (Q1-Q3)')
x_min_mantap = q1_tenure_yes_churn - 20
x_max_mantap = q3_tenure_yes_churn + 20
plt.xlim(x_min_mantap, x_max_mantap)
plt.axvspan(q3_tenure_yes_churn, x_max_mantap, color='green', alpha=0.2, label='IQR Area (Q1-Q3)')
plt.axvspan(q1_tenure_yes_churn, x_min_mantap, color='red', alpha=0.2, label='IQR Area (Q1-Q3)')

plt.text(q1_tenure_yes_churn - 10, 0.008, 'Trial doang\n(<Q1)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q3_tenure_yes_churn + 10 , 0.008, 'Loyal\n(> Q3)', ha='center', fontsize=11, fontweight='bold',bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q1_tenure_yes_churn + 20, 0.008, 'Safe\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold',color = 'darkred', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))

plt.title('Distribusi Tenure Pelanggan Churn (KDE) dengan IQR')
plt.xlabel('Tenure (bulan)')
plt.ylabel('Kepadatan')
plt.legend()
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(data=yes_churn, x='Contract', palette='Set2')
plt.title('Jenis Kontrak Pelanggan yang Churn')
plt.xlabel('Jenis Kontrak')
plt.ylabel('Jumlah Pelanggan')
ax = plt.gca()
ax.annotate(
    'Pelanggan yang churn\npaling banyak pada kontrak bulanan',  
    xy=(0.4, 1500),  
    xytext=(1.2, 1400),  
    arrowprops=dict(facecolor='steelblue', arrowstyle='->'),
    ha='center'
)
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\374669270.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=yes_churn, x='Contract', palette='Set2')

plt.figure(figsize=(8, 6))
sns.countplot(data=yes_churn, x='Status', palette='Set1')
plt.title('Status Pelanggan yang Churn')
plt.xlabel('Status Pelanggan')
plt.ylabel('Jumlah Pelanggan')
ax = plt.gca()
ax.annotate(
    'Pelanggan yang paling banyak tidak churn\nmerupakan pelanggan yang memiliki \nstatus "married"',  
    xy=(0.6, 900),  
    xytext=(0.03, 920),  
    arrowprops=dict(facecolor='steelblue', arrowstyle='->'),
    ha='center'
)
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\1942244962.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=yes_churn, x='Status', palette='Set1')

q1_monthly_charges_yes_churn = yes_churn['MonthlyCharges'].quantile(0.25)
q3_monthly_charges_yes_churn = yes_churn['MonthlyCharges'].quantile(0.75)
iqr_monthly_charges_yes_churn = q3_monthly_charges_yes_churn - q1_monthly_charges_yes_churn
lower_bound_monthly_charges_yes_churn = q1_monthly_charges_yes_churn - 1.5 * iqr_monthly_charges_yes_churn
upper_bound_monthly_charges_yes_churn = q3_monthly_charges_yes_churn + 1.5 * iqr_monthly_charges_yes_churn

plt.figure(figsize=(14, 6))
sns.kdeplot(data=yes_churn['MonthlyCharges'], fill=True, color='green')
plt.title('Distribusi Biaya Bulanan untuk Pelanggan yang Tidak Churn (KDE Plot)')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Kepadatan')

x_min_3 = q1_monthly_charges_yes_churn - 30
x_max_3= q3_monthly_charges_yes_churn + 50
plt.xlim(x_min_3, x_max_3)

plt.axvline(q1_monthly_charges_yes_churn, color='blue', linestyle='--', label='Q1')
plt.axvline(q3_monthly_charges_yes_churn, color='orange', linestyle='--', label='Q3')
plt.axvspan(q1_monthly_charges_yes_churn, q3_monthly_charges_yes_churn, color='red', alpha=0.2, label='IQR Area (Q1-Q3)')
plt.axvspan(x_min_3, q1_monthly_charges_yes_churn, color='blue', alpha=0.2, label='Safe Area (< Q1)')
plt.axvspan(q3_monthly_charges_yes_churn, x_max_3, color='yellow', alpha=0.2, label='Safe Area (> Q3)')
# plt.axvspan(upper_bound_monthly_charges_no_churn, no_churn['MonthlyCharges'].max(), color='green', alpha=0.2, label='Upper Bound Area (> Q3)')
plt.text(q1_monthly_charges_yes_churn -10, 0.008, 'Most Loyal\n(<Q1)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q3_monthly_charges_yes_churn + 20, 0.008, 'Safe\n(> Q3)', ha='center', fontsize=11, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.text(q1_monthly_charges_yes_churn + 30, 0.008, 'Low Loyal\n(Q1–Q3)', ha='center', fontsize=11, fontweight='bold', color='darkred', bbox=dict(facecolor='white', alpha=0.7, edgecolor='gray'))
plt.legend()
plt.show()

def label_tenure(t):
    if t <= 12:
        return '≤1 Tahun'
    elif t <= 24:
        return '1-2 Tahun'
    elif t <= 36:
        return '2-3 Tahun'
    elif t <= 48:
        return '3-4 Tahun'
    elif t <= 60:
        return '4-5 Tahun'
    else:
        return '>5 Tahun'

agung['TenureGroup'] = agung['tenure'].apply(label_tenure)

# Buat dua subset: churn dan tidak churn
tenure_yes_churn_pertahun = agung[agung['Churn'] == 'Yes']
tenure_no_churn_pertahun = agung[agung['Churn'] == 'No']

# Plot barchart jumlah pelanggan per grup tenure, dipisah churn dan tidak churn
fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

sns.countplot(data=tenure_yes_churn_pertahun, x='TenureGroup', order=['≤1 Tahun','1-2 Tahun','2-3 Tahun','3-4 Tahun','4-5 Tahun','>5 Tahun'], ax=axes[0], palette='Reds')
axes[0].set_title('Jumlah Pelanggan Churn per Kelompok Tenure')
axes[0].set_xlabel('Kelompok Tenure')
axes[0].set_ylabel('Jumlah Pelanggan')
axes[0].tick_params(axis='x', rotation=45)

sns.countplot(data=tenure_no_churn_pertahun, x='TenureGroup', order=['≤1 Tahun','1-2 Tahun','2-3 Tahun','3-4 Tahun','4-5 Tahun','>5 Tahun'], ax=axes[1], palette='Greens')
axes[1].set_title('Jumlah Pelanggan Tidak Churn per Kelompok Tenure')
axes[1].set_xlabel('Kelompok Tenure')
axes[1].set_ylabel('')
axes[1].tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3337457223.py:24: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=tenure_yes_churn_pertahun, x='TenureGroup', order=['≤1 Tahun','1-2 Tahun','2-3 Tahun','3-4 Tahun','4-5 Tahun','>5 Tahun'], ax=axes[0], palette='Reds')
C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3337457223.py:30: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=tenure_no_churn_pertahun, x='TenureGroup', order=['≤1 Tahun','1-2 Tahun','2-3 Tahun','3-4 Tahun','4-5 Tahun','>5 Tahun'], ax=axes[1], palette='Greens')

def label_monthly_charge(m):
    if m <= 40:
        return '<$40'
    elif m <= 80:
        return '$40-80'
    else:
        return '>$80'

agung['MonthlyGroup'] = agung['MonthlyCharges'].apply(label_monthly_charge)

# Buat dua subset berdasarkan Churn
bulanan_yes_churn = agung[agung['Churn'] == 'Yes']
bulanan_no_churn = agung[agung['Churn'] == 'No']


fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

sns.countplot(data=bulanan_yes_churn, x='MonthlyGroup', order=['<$40', '$40-80', '>$80'], ax=axes[0], palette='Reds')
axes[0].set_title('Jumlah Pelanggan Churn per Kelompok Biaya Bulanan')
axes[0].set_xlabel('Kelompok Biaya Bulanan')
axes[0].set_ylabel('Jumlah Pelanggan')

sns.countplot(data=bulanan_no_churn, x='MonthlyGroup', order=['<$40', '$40-80', '>$80'], ax=axes[1], palette='Greens')
axes[1].set_title('Jumlah Pelanggan Tidak Churn per Kelompok Biaya Bulanan')
axes[1].set_xlabel('Kelompok Biaya Bulanan')
axes[1].set_ylabel('')

plt.tight_layout()
plt.show()

C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3677862183.py:18: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=bulanan_yes_churn, x='MonthlyGroup', order=['<$40', '$40-80', '>$80'], ax=axes[0], palette='Reds')
C:\Users\Wahgus\AppData\Local\Temp\ipykernel_19720\3677862183.py:23: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(data=bulanan_no_churn, x='MonthlyGroup', order=['<$40', '$40-80', '>$80'], ax=axes[1], palette='Greens')

agung['Contract'] = agung['Contract'].astype(str)
agung['Churn'] = agung['Churn'].astype(str)

plt.figure(figsize=(10, 6))
sns.countplot(data=agung, x='Contract', hue='Churn', palette='Set2')

plt.title('Perbandingan Jenis Kontrak Pelanggan: Churn vs Tidak Churn')
plt.xlabel('Jenis Kontrak')
plt.ylabel('Jumlah Pelanggan')

# Menambahkan anotasi
plt.annotate(
    'Mayoritas pelanggan churn\nkontrak bulanan',
    xy=(0.38, 1600),
    xytext=(0.85, 1600),
    arrowprops=dict(facecolor='steelblue', arrowstyle='->'),
    ha='center'
)

plt.annotate(
    'Mayoritas pelanggan tidak churn\njuga pada kontrak bulanan',
    xy=(0, 2000),
    xytext=(0.6, 2100),
    arrowprops=dict(facecolor='green', arrowstyle='->'),
    ha='center'
)

plt.tight_layout()
plt.show()

agung.groupby(['Contract', 'Churn']).size().reset_index(name='JumlahPelanggan')

agung_otw_married = agung.groupby(['Status', 'Churn']).size().reset_index(name='Jumlah')

# Hitung total per Status untuk rasio
total_per_status = agung.groupby('Status').size().reset_index(name='Total')
agung_otw_married = agung_otw_married.merge(total_per_status, on='Status')
agung_otw_married['Rasio'] = agung_otw_married['Jumlah'] / agung_otw_married['Total']

plt.figure(figsize=(8, 6))
sns.barplot(data=agung_otw_married, x='Status', y='Rasio', hue='Churn', palette='deep')

plt.title('Rasio Churn terhadap Status Pelanggan')
plt.xlabel('Status Pelanggan')
plt.ylabel('Rasio Churn')
plt.legend(title='Churn')
plt.tight_layout()
plt.show()

agung_otw_married

Non_ideal_customer = agung[
    (agung['Contract'] == 'Month-to-month') &
    (agung['Status'] == 'Married') &
    (agung['tenure'] < 12) &
    (agung['MonthlyCharges'] >= 80) & 
    (agung['MonthlyCharges'] <= 120)
]


percNon_ideal_customer = Non_ideal_customer['Churn'].value_counts().reset_index()
percNon_ideal_customer.columns = ['Churn', 'JumlahPelanggan']



plt.figure(figsize=(8, 6))
plt.pie(percNon_ideal_customer['JumlahPelanggan'], labels=percNon_ideal_customer['Churn'], autopct='%1.1f%%', startangle=140)
plt.title('Segmen Pelanggan Non-Ideal')
plt.axis('equal')  
plt.tight_layout()
plt.legend(title='Status Churn')
plt.show()

ideal_customer = agung[(agung['tenure'] > 24) & (agung['Contract'] == 'Two year') & (agung['Status'] == 'Married') & (agung['MonthlyCharges'].between(40, 80))]



perc_ideal_customer = ideal_customer['Churn'].value_counts().reset_index()
perc_ideal_customer.columns = ['Churn', 'JumlahPelanggan']



plt.figure(figsize=(8, 6))
plt.pie(status_ideal_customer['JumlahPelanggan'], labels=perc_ideal_customer['Churn'], autopct='%1.1f%%', startangle=140)
plt.title('Segmen Pelanggan Ideal')
plt.axis('equal')  
plt.tight_layout()
plt.legend(title='Status Churn')
plt.show()

# Buat subplot untuk dua pie chart: pelanggan ideal vs non-ideal

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Pie chart untuk pelanggan ideal
axes[0].pie(
    perc_ideal_customer['JumlahPelanggan'],
    labels=perc_ideal_customer['Churn'],
    autopct='%1.1f%%',
    startangle=140,
    colors=['darkgreen', 'yellow']
)
axes[0].axis('equal')
axes[0].set_title('Segmen Pelanggan Ideal')

# Pie chart untuk pelanggan non-ideal
axes[1].pie(
    percNon_ideal_customer['JumlahPelanggan'],
    labels=percNon_ideal_customer['Churn'],
    autopct='%1.1f%%',
    startangle=140,
    colors=['yellow', 'darkgreen'] 
)
axes[1].axis('equal')
axes[1].set_title('Segmen Pelanggan Non-Ideal')

plt.suptitle('"Segmen Ini Hampir Kebal Churn, Kenapa Kita Tidak Fokus di Sini?"', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.legend(title='Status Churn', loc='upper right')
plt.show()

# Buat subplot untuk dua pie chart: pelanggan ideal vs non-ideal

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Pie chart untuk pelanggan ideal
axes[0].pie(
    perc_ideal_customer['JumlahPelanggan'],
    labels=perc_ideal_customer['Churn'],
    autopct='%1.1f%%',
    startangle=140,
    colors=['darkgreen', 'yellow']
)
axes[0].axis('equal')
axes[0].set_title('Segmen Pelanggan Ideal')

# Pie chart untuk pelanggan non-ideal
axes[1].pie(
    percNon_ideal_customer['JumlahPelanggan'],
    labels=percNon_ideal_customer['Churn'],
    autopct='%1.1f%%',
    startangle=140,
    colors=['yellow', 'darkgreen'] 
)
axes[1].axis('equal')
axes[1].set_title('Segmen Pelanggan Non-Ideal')
plt.suptitle('"Segmen Ini Hampir Kebal Churn, Kenapa Kita Tidak Fokus di Sini?"', fontsize=14, fontweight='bold', y=1.08)

fig.text(
    0.5, 0.98,
    'Empat kriteria sederhana kontrak jangka panjang, status pernikahan, masa pakai layanan, dan biaya bulanan \n ternyata mampu mengidentifikasi pelanggan ideal!',
    ha='center',
    fontsize=11
)

plt.tight_layout()
plt.legend(title='Status Churn', loc='upper right')
plt.show()

filtered = agung[(agung['Contract'] == 'Month-to-month') & (agung['Status'] == 'Married') &(agung['tenure'] < 12) &(agung['MonthlyCharges'] >= 80) & (agung['MonthlyCharges'] <= 120)]


hasil = filtered['Churn'].value_counts().reset_index()
hasil.columns = ['Churn', 'Jumlah']
print(hasil)

  Churn  Jumlah
0   Yes     161
1    No      54

filtered = agung[(agung['Contract'] == 'Month-to-month') &(agung['Status'] == 'Married') &(agung['tenure'] < 12) &(agung['MonthlyCharges'] >= 80) & agung['MonthlyCharges'] <= 120)]


hasil = filtered['Churn'].value_counts().reset_index()
hasil.columns = ['Churn', 'Jumlah']


total = hasil['Jumlah'].sum()
hasil['Rasio (%)'] = (hasil['Jumlah'] / total * 100).round(2)


print(hasil)

  Churn  Jumlah  Rasio (%)
0   Yes     161      74.88
1    No      54      25.12

ideal_customer = agung[(agung['tenure'] > 24) & (agung['Contract'] == 'Two year') & (agung['Status'] == 'Married') & (agung['MonthlyCharges'].between(40, 80))]

status_ideal_customer = ideal_customer['Churn'].value_counts().reset_index()
status_ideal_customer.columns = ['Churn', 'JumlahPelanggan']
status_ideal_customer

	count	mean	std	min	25%	50%	75%	max
PaymentMethod
Bank transfer (automatic)	1544.0	67.192649	30.555200	18.40	41.0875	73.100	92.9625	118.75
Credit card (automatic)	1522.0	66.512385	30.612424	18.25	38.6625	73.025	90.8500	117.50
Electronic check	2365.0	76.255814	24.053655	18.85	60.1500	80.550	94.7000	118.65
Mailed check	1612.0	43.917060	26.314665	18.70	20.1500	34.700	64.9125	118.60

	count	mean	std	min	25%	50%	75%	max
PaymentMethod
Bank transfer (automatic)	1544.0	67.192649	30.555200	18.40	41.0875	73.100	92.9625	118.75
Credit card (automatic)	1522.0	66.512385	30.612424	18.25	38.6625	73.025	90.8500	117.50
Electronic check	2365.0	76.255814	24.053655	18.85	60.1500	80.550	94.7000	118.65
Mailed check	1612.0	43.917060	26.314665	18.70	20.1500	34.700	64.9125	118.60

	SeniorCitizen	PhoneService	JumlahCustomerYangSeniorplusLanggananTelepon	SemuaCustomer	Persentase
0	0	No	578	5901	9.79
1	0	Yes	5323	5901	90.21
2	1	No	104	1142	9.11
3	1	Yes	1038	1142	90.89

	customerID	gender	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

	count	mean	std	min	25%	50%	75%	max
Churn
No	5174.0	61.265124	31.092648	18.25	25.10	64.425	88.4	118.75
Yes	1869.0	74.441332	24.666053	18.85	56.15	79.650	94.2	118.35

	Contract	Churn	JumlahPelanggan
0	Month-to-month	No	2220
1	Month-to-month	Yes	1655
2	One year	No	1307
3	One year	Yes	166
4	Two year	No	1647
5	Two year	Yes	48

	Status	Churn	Jumlah	Total	Rasio
0	Married	No	3302	4324	0.763645
1	Married	Yes	1022	4324	0.236355
2	Single	No	1872	2719	0.688488
3	Single	Yes	847	2719	0.311512

Homework Data Visualization¶

Soal 1¶

Interpretasi¶

Interquartile Range (IQR) dan Batas Outlier per Metode Pembayaran¶

Implikasi Bisnis Berdasarkan IQR dan Distribusi Biaya Bulanan¶

1. Segmen “Kelas Atas / Mytic Glory” : Electronic Check¶

2. Segmen “Stabil Beragam” : Autodebet (Bank Transfer & Credit Card)¶

3. Segmen “Kelas Epic / Konservatif” : Mailed Check¶

Implikasi Bisnis untuk Pihak Bank¶

Strategi Layanan Keuangan yang Tepat Sasaran¶

Peningkatan Fee Based Income melalui Kolaborasi¶

Soal 2¶

Alasan Penggunaan Barchart¶

interpretasi¶

Soal 3¶

Interpretasi¶

Soal 4¶

interpretasi¶

Siapa yang benar-benar menguntungkan?¶

Mainstream = Volume Besar, Stabilitas Tinggi¶

VIP = Volume Kecil, Potensi Margin Tinggi¶

Peluang Segmentasi & Personalisasi¶

Mendorong Pelanggan Mainstream ke VIP¶

Soal 5¶

Interpretasi¶

Uji Mann-Whiteney¶

IQR¶

"Siapa pelanggan yang akan pergi, dan siapa yang layak dipertahankan?"¶

1. Churn = Yes → Mereka yang Akan Pergi¶

2. Churn = No → Mereka yang Bertahan¶

Implikasi Bisnis: Data yang Bicara, Strategi yang Bekerja¶

Soal 6¶

Interpretasi: Pengaruh Memiliki Pasangan & Tanggungan terhadap Tingkat Churn Pelanggan¶

Temuan Utama:¶

Kesimpulan¶

Soal 7¶

Interpretasi¶

Jenis Kontrak¶

1. Kontrak Bulanan (Month-to-Month)¶

2. Kontrak Tahunan (One Year)¶

3. Kontrak Dua Tahun (Two Year)¶

Status Pelanggan¶

1. Pelanggan Berstatus Married¶

2. Pelanggan Berstatus Single¶

Tenure (Durasi Berlangganan)¶

1. Pelanggan Tidak Churn¶

2. Pelanggan Churn¶

Monthly Charges (Tagihan Bulanan)¶

Pelanggan Tidak Churn¶

Pelanggan Churn¶

Implikasi Bisnis¶

Ditemukan bahwa dari total 279 pelanggan, sebanyak 270 pelanggan tidak churn (96.77%) dan hanya 9 pelanggan yang churn (3.23%).¶

`Homework Data Visualization`¶

`Soal 1`¶

`Interpretasi`¶

`Soal 2`¶

`Alasan Penggunaan Barchart`¶

`interpretasi`¶

`Soal 3`¶

`Interpretasi`¶

`Soal 4`¶

`interpretasi`¶

`Siapa yang benar-benar menguntungkan?`¶

`Soal 5`¶

`Interpretasi`¶

`"Siapa pelanggan yang akan pergi, dan siapa yang layak dipertahankan?"`¶

`Soal 6`¶

`Interpretasi`: Pengaruh Memiliki Pasangan & Tanggungan terhadap Tingkat Churn Pelanggan¶

`Kesimpulan`¶

`Soal 7`¶

`Interpretasi`¶

`Implikasi Bisnis`¶